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        <title>Improving a Strong Neural Parser with Conjunction-Specific Features</title>
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        <namePart type="given">Jessica</namePart>
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            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
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        <name type="personal">
            <namePart type="given">Alexander</namePart>
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    <abstract>While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others. In particular, dependency parsers perform poorly in coordination construction (i.e., correctly attaching the conj relation). We extend a state-of-the-art dependency parser with conjunction-specific features, focusing on the similarity between the conjuncts head words. Training the extended parser yields an improvement in conj attachment as well as in overall dependency parsing accuracy on the Stanford dependency conversion of the Penn TreeBank.</abstract>
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%0 Conference Proceedings
%T Improving a Strong Neural Parser with Conjunction-Specific Features
%A Ficler, Jessica
%A Goldberg, Yoav
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F ficler-goldberg-2017-improving
%X While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others. In particular, dependency parsers perform poorly in coordination construction (i.e., correctly attaching the conj relation). We extend a state-of-the-art dependency parser with conjunction-specific features, focusing on the similarity between the conjuncts head words. Training the extended parser yields an improvement in conj attachment as well as in overall dependency parsing accuracy on the Stanford dependency conversion of the Penn TreeBank.
%U https://aclanthology.org/E17-2055
%P 343-348

Markdown (Informal)

[Improving a Strong Neural Parser with Conjunction-Specific Features](https://aclanthology.org/E17-2055) (Ficler & Goldberg, EACL 2017)

• Improving a Strong Neural Parser with Conjunction-Specific Features (Ficler & Goldberg, EACL 2017)

ACL

• Jessica Ficler and Yoav Goldberg. 2017. Improving a Strong Neural Parser with Conjunction-Specific Features. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 343–348, Valencia, Spain. Association for Computational Linguistics.